import uuid
import string
import json
import time
import os
import datetime
import base64

from openpyxl.workbook import Workbook
from openpyxl.styles import Font, Alignment, Side, Border
from openpyxl.drawing import image

from Lib.Utils import Utils


class ExportReport:

    def __init__(self, test_time, test_materials, set_temperature,
                 set_intensity, set_interval, test_people, file_path):
        """
        :param test_time:  试验时间
        :param test_materials:  试验材料
        :param set_temperature:  设定温度
        :param set_intensity: 设置强度
        :param set_interval: 设定间隔
        :param test_people: 试验人
        :param file_path: 读取json数据的文件路径
        """
        self.test_time = test_time
        self.test_materials = test_materials
        self.set_temperature = str(set_temperature) + ' ℃'
        self.set_intensity = str(set_intensity) + ' μW/cm2'
        self.set_interval = str(set_interval) + ' h'
        self.test_people = test_people
        self.file_path = file_path
        # base64转化为图片
        # self.bs64 = bs64
        # self.img_path = Utils.change_base64_as_img(self.bs64)
        self.wb = Workbook()
        self.wb.remove(self.wb['Sheet'])
        # 创建第一个sheet
        self.ws = self.wb.create_sheet('流出杯试验报表', 0)
        self.upper4_list = string.ascii_uppercase[:4]
        left, right, top, bottom = [Side(style='thin', color='000000')] * 4
        self.border_style = Border(left=left, right=right, top=top, bottom=bottom)
        try:
            self.set_style()
        except Exception as e:
            print('报表异常', str(e))

    # 定义边框样式
    def set_style(self):
        # 设置每一列的宽
        self.ws.column_dimensions['A'].width = 22
        self.ws.column_dimensions['B'].width = 22
        self.ws.column_dimensions['C'].width = 22
        self.ws.column_dimensions['D'].width = 22
        self.create_row1()
        self.create_row2()
        self.create_row3_6()
        self.create_row7()
        self.create_many_rows()

    def create_row1(self):
        # 创建第一行样式并写入值
        self.ws.merge_cells(start_row=1, end_row=1, start_column=1, end_column=4)
        self.ws.row_dimensions[1].height = 33
        self.ws.cell(row=1, column=1).font = Font(size=20, bold=True)
        self.ws.cell(row=1, column=1).alignment = Alignment(horizontal='center', vertical='center')
        self.ws.cell(row=1, column=1).value = '光解试验仪数据报表'

    def create_row2(self):
        # 创建第2行
        self.ws.merge_cells(start_row=2, end_row=2, start_column=1, end_column=4)
        self.ws.cell(row=2, column=1).font = Font(size=13, bold=True)
        self.ws.cell(row=2, column=1).value = '试验基本信息'
        self.ws.row_dimensions[2].height = 20
        self.ws.cell(row=2, column=1).alignment = Alignment(vertical='center')

    def create_row3_6(self):
        # 定义第3-6的行高
        for index in range(3, 6):
            self.ws.row_dimensions[index].height = 28
            for col_ in range(1, len(self.upper4_list) + 1):
                self.ws.cell(row=index, column=col_).font = Font(size=12)
                self.ws.cell(row=index, column=col_).alignment = Alignment(horizontal='center', vertical='center')
        # 写入第三行
        self.ws.cell(row=3, column=1).value = '试验时间:'
        self.ws.cell(row=3, column=2).value = self.test_time
        self.ws.cell(row=3, column=3).value = '试验材料:'
        self.ws.cell(row=3, column=4).value = self.test_materials

        # 写入第四行
        self.ws.cell(row=4, column=1).value = '设定环境温度:'
        self.ws.cell(row=4, column=2).value = self.set_temperature
        self.ws.cell(row=4, column=3).value = '设定光照强度:'
        self.ws.cell(row=4, column=4).value = self.set_intensity

        # 写入第五行
        self.ws.cell(row=5, column=1).value = '设定取样间隔:'
        self.ws.cell(row=5, column=2).value = self.set_interval
        self.ws.cell(row=5, column=3).value = '试验人:'
        self.ws.cell(row=5, column=4).value = self.test_people

        # 写入第6行
        self.ws.merge_cells(start_row=6, end_row=6, start_column=1, end_column=4)
        self.ws.cell(row=6, column=1).value = '试验记录信息'
        self.ws.cell(row=6, column=1).font = Font(size=13, bold=True)
        self.ws.cell(row=6, column=1).alignment = Alignment(vertical='center')
        self.ws.row_dimensions[6].height = 20

    def create_row7(self):

        # 写入第7行
        self.ws.cell(row=7, column=1).value = '记录时间 h'
        self.ws.cell(row=7, column=2).value = '温度值 ℃'
        self.ws.cell(row=7, column=3).value = '记录时间 h'
        self.ws.cell(row=7, column=4).value = '光照值 μW/cm2'

        # 为前7行加上固定边框样式
        for row_ in range(1, 8):
            for index in range(1, len(self.upper4_list) + 1):
                self.ws.cell(row=row_, column=index).border = self.border_style
                if row_ == 7:
                    self.ws.cell(row=row_, column=index).alignment = Alignment(horizontal='center',
                                                                               vertical='center')
                    self.ws.cell(row=row_, column=index).font = Font(size=12)

    def create_many_rows(self):
        # 写入第7行以后的其他多行
        print(self.file_path + '/1.json')
        with open(self.file_path + '/1.json', 'r') as f:
            content_list1 = json.loads(f.read())
        with open(self.file_path + '/2.json', 'r') as f:
            content_list2 = json.loads(f.read())

        for index in range(8, len(content_list1) + 8):

            time_convert = time.strftime('%Y/%m/%d %H:%M:%S', time.localtime(content_list1[index - 8][0] // 1000))
            self.ws.cell(row=index, column=1).value = time_convert
            self.ws.cell(row=index, column=2).value = content_list1[index - 8][1]
            self.ws.cell(row=index, column=3).value = time_convert
            self.ws.cell(row=index, column=4).value = content_list2[index - 8][1]
            for col_ in range(1, 5):
                self.ws.cell(row=index, column=col_).alignment = Alignment(horizontal='center', vertical='center')
                self.ws.cell(row=index, column=col_).font = Font(size=12)
                self.ws.cell(row=index, column=col_).border = self.border_style

    # def add_img(self,):
    #
    #     img = image.Image(self.img_path)
    #     new_size = (704, 560)
    #     img.width, img.height = new_size
    #     # 空1格当做隔断
    #     self.ws.add_image(img, 'A' + str(self.ws.max_row + 2))

    def save(self, filename):
        try:
            self.wb.save(filename)
        except:
            self.wb.save(filename[:-5] + str('_' + Utils.getFileName()) + filename[-5:])

        # 关闭excel
        self.close()

        # 删除img图片
        # os.remove(self.img_path)

    def close(self):
        self.wb.close()


if __name__ == '__main__':
    str1 = 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'

    a = ExportReport('2018-11-20 09:27:22', '试样1', '20.0', '20.0', '1', 'admin', '/home/pi/Documents/App/TY_Photolysis/Resource/ExperimentTemp/6dbf486e-ec63-11e8-8051-7427eac89794', str1)
    a.save('1.xlsx')


